Clustering and Classification of Satellite Imagery

• Load a raster file, which has 4 layers

rasterJ<-brick("C:\\Users\\D059331\\Desktop\\DM GIC\\data\\img\\J_04AUG14112729-M2AS-000000137917_01_P001_etrs89.TIF")
rasterJ

## class       : RasterBrick 
## dimensions  : 4238, 4239, 17964882, 4  (nrow, ncol, ncell, nlayers)
## resolution  : 2.4, 2.4  (x, y)
## extent      : -105092.3, -94918.74, -50088.84, -39917.64  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=tmerc +lat_0=39.66825833333333 +lon_0=-8.133108333333334 +k=1 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs 
## data source : C:\Users\D059331\Desktop\DM GIC\data\img\J_04AUG14112729-M2AS-000000137917_01_P001_etrs89.TIF 
## names       : J_04AUG14112729.M2AS.000000137917_01_P001_etrs89.1, J_04AUG14112729.M2AS.000000137917_01_P001_etrs89.2, J_04AUG14112729.M2AS.000000137917_01_P001_etrs89.3, J_04AUG14112729.M2AS.000000137917_01_P001_etrs89.4 
## min values  :                                                  0,                                                  0,                                                  0,                                                  0 
## max values  :                                                255,                                                255,                                                255,                                                255

• Plot map

plotRGB(rasterJ, 3,2,1)

• choose a sector for clustering

ext <- extent(-104294.4, -102964.5, -43623.48, -42742.44 )
sector <- crop(rasterJ, ext)
plotRGB(sector, 3, 2, 1)

• add normdiff layer for better vegetation recognition

sector[[5]] <- normdiff(sector)

• plot each layer separately

plot(sector)

• check for NAs

summary(sector)

##         J_04AUG14112729.M2AS.000000137917_01_P001_etrs89.1
## Min.                                                    23
## 1st Qu.                                                 31
## Median                                                  34
## 3rd Qu.                                                 40
## Max.                                                   176
## NA's                                                     0
##         J_04AUG14112729.M2AS.000000137917_01_P001_etrs89.2
## Min.                                                    26
## 1st Qu.                                                 40
## Median                                                  50
## 3rd Qu.                                                 65
## Max.                                                   255
## NA's                                                     0
##         J_04AUG14112729.M2AS.000000137917_01_P001_etrs89.3
## Min.                                                     8
## 1st Qu.                                                 17
## Median                                                  34
## 3rd Qu.                                                 51
## Max.                                                   194
## NA's                                                     0
##         J_04AUG14112729.M2AS.000000137917_01_P001_etrs89.4      values
## Min.                                                     4 -0.64885496
## 1st Qu.                                                 10 -0.20000000
## Median                                                  53  0.05645161
## 3rd Qu.                                                 70  0.15384615
## Max.                                                   191  0.76870748
## NA's                                                     0  0.00000000

• use only nir and ndvi layer for k-means

sector_mod <- brick(sector[[4]],sector[[5]])

• perform kmeans and plot result

performKMeans(sector_mod, 12)

K-Means doesnt seem to perform very well on this sector. Ocean and land arent clearly separated. Maybe this works better with fewer clusters (i.e. lower k).

performKMeans(sector_mod, 6)

Lets try instead a different sector, one without ocean.

ext <- extent(-101751, -100703.7, -47812.61, -46914.94)
landSector <- crop(rasterJ, ext)
landSector[[5]] <- normdiff(landSector)
plotRGB(landSector, 3, 2, 1)

landSector_mod <- brick(landSector[[4]],landSector[[5]])
performKMeans(landSector_mod, 12)

This is not too awesome. Try 6 clusters.

performKMeans(landSector_mod, 12)